Senior-COVID-Rea Cohort Study: A Geriatric Prediction Model of 30-day Mortality in Patients Aged over 60 Years in ICU for Severe COVID-19

The SARS-COV2 pandemic induces tensions on health systems and ethical dilemmas. Practitioners need help tools to define patients not candidate for ICU admission. A multicentre observational study was performed to evaluate the impact of age and geriatric parameters on 30-day mortality in patients aged ≥60 years of age. Patients or next of kin were asked to answer a phone questionnaire assessing geriatric covariates 1 month before ICU admission. Among 290 screened patients, 231 were included between March 7 and May 7, 2020. In univariate, factors associated with lower 30-day survival were: age (per 10 years increase; OR 3.43, [95%CI: 2.13-5.53]), ≥3 CIRS-G grade ≥2 comorbidities (OR 2.49 [95%CI: 1.36-4.56]), impaired ADL, (OR 4.86 [95%CI: 2.44-9.72]), impaired IADL8 (OR 6.33 [95%CI: 3.31-12.10], p<0.001), frailty according to the Fried score (OR 4.33 [95%CI: 2.03-9.24]) or the CFS ≥5 (OR 3.79 [95%CI: 1.76-8.15]), 6-month fall history (OR 3.46 [95%CI: 1.58-7.63]). The final multivariate model included age (per 10 years increase; 2.94 [95%CI:1.78-5.04], p<0.001) and impaired IADL8 (OR 5.69 [95%CI: 2.90-11.47], p<0.001)). Considered as continuous variables, the model led to an AUC of 0.78 [95% CI: 0.72, 0.85]. Age and IADL8 provide independent prognostic factors for 30-day mortality in the considered population. Considering a risk of death exceeding 80% (82.6% [95%CI: 61.2% - 95.0%]), patients aged over 80 years with at least 1 IADL impairment appear as poor candidates for ICU admission.


Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 7 According to recent evidence from two studies, disease outcomes of COVID-19 patients admitted to hospital would be better predicted by frailty than either age or comorbidity.
Objectives 3 State specific objectives, including any prespecified hypotheses 8 we conducted a multicentre observational study to determine the clinical and biological covariates predictive of mortality in the population of patients over 60 years of age admitted in intensive care unit, with a specific attention paid to a retrospective and declarative assessment of their geriatric parameters 1 month before  infection. This first analysis explores the respective impacts of various geriatric parameters and discusses their respective properties.

Study design 4
Present key elements of study design early in the paper 9 Senior-COVID-Rea study is a multicenter observational cohort study Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection 9 Data were collected across seven ICUs in Auvergne Rhone Alpes Region, France. A standardised case report format was used for recording data collected.
Participants 6 (a) Cohort study-Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up Case-control study-Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls Cross-sectional study-Give the eligibility criteria, and the sources and methods of selection of participants 9 All patients aged 60 or older admitted to the participating ICUs with a diagnosis of COVID-19 were screened and included provided their (or their relative's) agreement. Diagnostic criteria were laboratory-confirmed SARS-CoV-2-positive swabs or a radiological diagnosis made by lung CT-scan consistent with COVID-19. Patients were excluded during data analysis only when duplicates were found, due to patients transferred from one ICU to the other from the participating centres. No other exclusion criteria were applied.
(b) Cohort study-For matched studies, give matching criteria and number of exposed and unexposed Statistical bias: The overlap between the different factors was analyzed through a Venn diagram, using the categorized version of the different factors. Moreover, during the multivariate analyses, the collinearity between factors was analyzed through variance inflation factors (VIF). The ability of the last model obtained to identified patients that died during the 30 days following ICU admission was quantified by the area under the ROC curve (AUC); it was compared to the AUC of age alone. A 5-fold crossvalidation of the AUC of the model obtained was performed to assess the optimism of this model.
No imputation of missing variables was performed. P-values less than 0.05 were considered significant.
Study size 10 Explain how the study size was arrived at 10 The first hypothesis of Senior-COVID-Rea was based of the first results of first Chinese retrospective results (1): considering a single analysis variable (age), with expected mortality of 30% in patients under 70 years of age, and 70% in patients over 70 years of age (with 40% of patients over 70 years of age), a total of 130 patients was expected to show a statistically significant difference between these two groups with a power of 90% (bilateral alpha risk test of 5%). Since the analysis considered the integration of several factors, considering 15 factors, hoping for a coefficient of determination of 0.5 of the model, to achieve an optimism of less than 10%, 185 patients were to be included (criterion 1 of Riley, Snell et al, (15)).
After the publication of data on mortality in ICU in Lombardy region, Italy in April 2020 (2), considering that a stopping of the trial at 185 patients would impair its statistical power and induce a potential risk of patients' selection bias, the scientific committee decided, on the 7th May, that all the patients admitted to ICU before that date -that corresponded to the end of the first COVID waveshould be screened and proposed the study without any patients' number limitation.
This sample size calculation was modified on Clinicaltrials.gov site accordingly (July 28, 2020).

Quantitative variables 11
Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why 10 Continuous variables were described by the mean, standard deviation (SD), and range. Categorized variables were described by the frequency and percentage of each modality. Subgroup analyses are not relevant in this study, but a cross-validation analysis was performed to assess the optimism of the model.   *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and crosssectional studies.

Introduction
Background and objectives 3a Explain the medical context (including whether diagnostic or prognostic) and rationale for developing or validating the multivariable prediction model, including references to existing models. 7-8 3b Specify the objectives, including whether the study describes the development or validation of the model or both. 8

Source of data 4a
Describe the study design or source of data (e.g., randomized trial, cohort, or registry data), separately for the development and validation data sets, if applicable. 9-10 4b Specify the key study dates, including start of accrual; end of accrual; and, if applicable, end of follow-up. 11 Participants 5a Specify key elements of the study setting (e.g., primary care, secondary care, general population) including number and location of centres. 9, 17 5b Describe eligibility criteria for participants. 9 5c Give details of treatments received, if relevant. na Outcome 6a Clearly define the outcome that is predicted by the prediction model, including how and when assessed. 6b Report any actions to blind assessment of the outcome to be predicted. na

Predictors 7a
Clearly define all predictors used in developing or validating the multivariable prediction model, including how and when they were measured. 10 7b Report any actions to blind assessment of predictors for the outcome and other predictors. na Sample size 8 Explain how the study size was arrived at. 10 Missing data 9 Describe how missing data were handled (e.g., complete-case analysis, single imputation, multiple imputation) with details of any imputation method. 11 Statistical analysis methods 10a Describe how predictors were handled in the analyses. 10, 11 10b Specify type of model, all model-building procedures (including any predictor selection), and method for internal validation.

10, 11
10d Specify all measures used to assess model performance and, if relevant, to compare multiple models. 10, 11 Risk groups 11 Provide details on how risk groups were created, if done. 10 Results

Participants 13a
Describe the flow of participants through the study, including the number of participants with and without the outcome and, if applicable, a summary of the followup time. A diagram may be helpful.

11
( Figure  1) 13b Describe the characteristics of the participants (basic demographics, clinical features, available predictors), including the number of participants with missing data for predictors and outcome.
11 (Table  1) Model development 14a Specify the number of participants and outcome events in each analysis. 11 (table  3) 14b If done, report the unadjusted association between each candidate predictor and outcome.
12 (table  4) Model specification 15a Present the full prediction model to allow predictions for individuals (i.e., all regression coefficients, and model intercept or baseline survival at a given time point).

Limitations 18
Discuss any limitations of the study (such as nonrepresentative sample, few events per predictor, missing data). 14-16